(1) Field of Invention
The present invention relates to a system for health prognosis of a component and, more particularly, to a system for health prognosis of a component based on a structured Bayesian network model which utilizes domain knowledge and system performance data.
(2) Description of Related Art
Current prognosis solutions are limited to individual components or simple subsystems (e.g., bearings, turbine disks, electric motors, and batteries). The reasoning in these systems is customized to the application and often based on simple heuristics.
For instance, in “Prognostics, Real Issues Involved with Predicting Life Remaining,” by S. Engel et al. in Proceedings of the IEEE Aerospace Conference, 2000, a tutorial describing in general terms a prognosis problem is presented. Engel et al. propose a prognosis within a probabilistic framework and defines key issues involved in applying probabilistic approaches to prognosis. The reference is theoretical in nature and does not provide specific solutions or algorithms.
Additionally, “An Open Systems Architecture for Prognostic Inference during Condition-Based Monitoring,” by G. Provan, in Proc. of the IEEE Aerospace Conference, 2003, describes an open systems architecture representation that is critical to any analysis of prognosis by specifying a generic prognosis module, the inputs and outputs to it, measures of remaining useful life, and the importance of how a component will be used. However, the reference is unclear regarding the details of the various components of its architecture. In particular, Provan does not provide any insight into the exact nature of how the observations will be integrated into a prognostic framework.
There are several solutions proposed in the literature that focus on applying the general framework for a particular application and developing prognostic solutions for specific subsystems. The first reference described below belongs to the former category, while the following two references belong to the latter category.
In “Prognostic Enhancements to Gas Turbine Diagnostic systems,” by C. S. Byington, et al. in Proc. of the IEEE Aerospace Conference, 2003, the authors apply a general framework to a specific application for gas turbine engine diagnosis.
“eSTORM: Enhanced Self Tuning On-board Real-Time Engine Model,” by T. Brotherton, et al. in Proc. of the IEEE Aerospace Conference, 2003, discloses a method for on-line diagnostics and prognostics. Brotherton, et al. upgrade a physics-based model called STORM with an empirical neural network, such that modeling errors during on-line functions are mitigated. The model is for an aircraft engine, and the results are specific for the aircraft engine. Brotherton, et al. focus on a narrow aspect of trending for a subsystem and do not address the crucial aspect of how various components for prognosis can be integrated into a single framework.
In “Nonparametric Modeling of Vibration Signal Features for Equipment Health Monitoring” in Proc. of the IEEE Aerospace Conference, 2003, S. W. Wegerich describes a method for modeling vibrations of systems from data. The reference also provides a method for evaluating if the vibrations are predictive of impending failures and provides an approach to the computation of the Useful Life Remaining of equipment based on the residual errors computed from the vibration characteristics. The reference is very narrow in that it models a specific component of a subsystem.
Thus, a continuing need exists for a systematic, application-independent approach to prognosis which is mathematically rigorous, efficient, and accurate.